Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5593-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5593-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 Jul 2022
Model description paper |
| 21 Jul 2022
| 21 Jul 2022
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
Julien Ruffault
INRAE, URFM, 84000 Avignon, France
François Pimont
INRAE, URFM, 84000 Avignon, France
Hervé Cochard
Université Clermont Auvergne, INRAE, PIAF, 63000
Clermont-Ferrand, France
Jean-Luc Dupuy
INRAE, URFM, 84000 Avignon, France
Nicolas Martin-StPaul
CORRESPONDING AUTHOR
INRAE, URFM, 84000 Avignon, France
Related authors
Julien Ruffault, Thomas Curt, Nicolas K. Martin-StPaul, Vincent Moron, and Ricardo M. Trigo
Nat. Hazards Earth Syst. Sci., 18, 847–856, https://doi.org/10.5194/nhess-18-847-2018, https://doi.org/10.5194/nhess-18-847-2018, 2018
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Extreme wildfires events (EWE) have been recorded during the past year in the Mediterranean. By analyzing the climatic conditions associated with the French 2003 and 2016 fires seasons, we found that EWE were associated to two distinct climatic events whose frequencies are both expected to increase with global changes: hot droughts and long droughts. These results suggest that EWE are likely to become more common in the future and will certainly challenge fire management.
Tanguy Postic, François de Coligny, Isabelle Chuine, Louis Devresse, Daniel Berveiller, Hervé Cochard, Matthias Cuntz, Nicolas Delpierre, Émilie Joetzjer, Jean-Marc Limousin, Jean-Marc Ourcival, François Pimont, Julien Ruffault, Guillaume Simioni, Nicolas K. Martin-StPaul, and Xavier Morin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2110, https://doi.org/10.5194/egusphere-2025-2110, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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PHOREAU is a forest dynamic model that links plant traits with water use, growth, and climate responses to explore how species diversity affects productivity and resilience. Validated across European forests, PHOREAU simulates how tree communities function under drought and warming. Our findings support the use of trait-based modeling to guide forest adaptation strategies under future climate scenarios.
Arsène Druel, Julien Ruffault, Hendrik Davi, André Chanzy, Olivier Marloie, Miquel De Cáceres, Albert Olioso, Florent Mouillot, Christophe François, Kamel Soudani, and Nicolas K. Martin-StPaul
Biogeosciences, 22, 1–18, https://doi.org/10.5194/bg-22-1-2025, https://doi.org/10.5194/bg-22-1-2025, 2025
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Accurate radiation data are essential for understanding ecosystem functions and dynamics. Traditional large-scale data lack the precision needed for complex terrain. This study introduces a new model, which accounts for sub-daily direct and diffuse radiation effects caused by terrain features, to enhance the radiation data resolution using elevation maps. Tested on a mountainous area, this method significantly improved radiation estimates, benefiting predictions of forest functions.
Marco M. Lehmann, Josie Geris, Ilja van Meerveld, Daniele Penna, Youri Rothfuss, Matteo Verdone, Pertti Ala-Aho, Matyas Arvai, Alise Babre, Philippe Balandier, Fabian Bernhard, Lukrecija Butorac, Simon Damien Carrière, Natalie C. Ceperley, Zuosinan Chen, Alicia Correa, Haoyu Diao, David Dubbert, Maren Dubbert, Fabio Ercoli, Marius G. Floriancic, Teresa E. Gimeno, Damien Gounelle, Frank Hagedorn, Christophe Hissler, Frédéric Huneau, Alberto Iraheta, Tamara Jakovljević, Nerantzis Kazakis, Zoltan Kern, Karl Knaebel, Johannes Kobler, Jiří Kocum, Charlotte Koeber, Gerbrand Koren, Angelika Kübert, Dawid Kupka, Samuel Le Gall, Aleksi Lehtonen, Thomas Leydier, Philippe Malagoli, Francesca Sofia Manca di Villahermosa, Chiara Marchina, Núria Martínez-Carreras, Nicolas Martin-StPaul, Hannu Marttila, Aline Meyer Oliveira, Gaël Monvoisin, Natalie Orlowski, Kadi Palmik-Das, Aurel Persoiu, Andrei Popa, Egor Prikaziuk, Cécile Quantin, Katja T. Rinne-Garmston, Clara Rohde, Martin Sanda, Matthias Saurer, Daniel Schulz, Michael Paul Stockinger, Christine Stumpp, Jean-Stéphane Venisse, Lukas Vlcek, Stylianos Voudouris, Björn Weeser, Mark E. Wilkinson, Giulia Zuecco, and Katrin Meusburger
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-409, https://doi.org/10.5194/essd-2024-409, 2024
Preprint under review for ESSD
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This study describes a unique large-scale isotope dataset to study water dynamics in European forests. Researchers collected data from 40 beech and spruce forest sites in spring and summer 2023, using a standardized method to ensure consistency. The results show that water sources for trees change between seasons and vary by tree species. This large dataset offers valuable information for understanding plant water use, improving ecohydrological models, and mapping water cycles across Europe.
Kaiyan Hu, Bertille Loiseau, Simon D. Carrière, Nolwenn Lesparre, Cédric Champollion, Nicolas K. Martin-StPaul, Niklas Linde, and Damien Jougnot
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-240, https://doi.org/10.5194/hess-2024-240, 2024
Revised manuscript accepted for HESS
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This study explores the potential of the electrical self-potential (SP) method, a passive geophysical technique, to provide additional insights into tree transpiration rates. We measured SP and sap velocity in three tree species over a year in a Mediterranean climate. Results indicate SP effectively characterizes transpiration rates, especially during dry seasons. Additionally, the electrokinetic coupling coefficients of these trees align with values typically found in porous geological media.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Julien Ruffault, Thomas Curt, Nicolas K. Martin-StPaul, Vincent Moron, and Ricardo M. Trigo
Nat. Hazards Earth Syst. Sci., 18, 847–856, https://doi.org/10.5194/nhess-18-847-2018, https://doi.org/10.5194/nhess-18-847-2018, 2018
Short summary
Short summary
Extreme wildfires events (EWE) have been recorded during the past year in the Mediterranean. By analyzing the climatic conditions associated with the French 2003 and 2016 fires seasons, we found that EWE were associated to two distinct climatic events whose frequencies are both expected to increase with global changes: hot droughts and long droughts. These results suggest that EWE are likely to become more common in the future and will certainly challenge fire management.
Related subject area
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Physical–biogeochemical ocean global models are required to analyze difficult oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
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This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
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Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
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We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
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This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
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Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
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The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
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Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
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Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Cited articles
Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J. R., Clarke, H.,
Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J.,
Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B.
P., Tapper, N. J., and Boer, M. M.: Connections of climate change and
variability to large and extreme forest fires in southeast Australia,
Commun. Earth Environ., 2, 1–17, https://doi.org/10.1038/s43247-020-00065-8, 2021.
Adams, H. D., Zeppel, M. J. B., Anderegg, W. R. L., Hartmann, H.,
Landhäusser, S. M., Tissue, D. T., Huxman, T. E., Hudson, P. J., Franz,
T. E., Allen, C. D., Anderegg, L. D. L., Barron-Gafford, G. A., Beerling, D.
J., Breshears, D. D., Brodribb, T. J., Bugmann, H., Cobb, R. C., Collins, A.
D., Dickman, L. T., Duan, H., Ewers, B. E., Galiano, L., Galvez, D. A.,
Garcia-Forner, N., Gaylord, M. L., Germino, M. J., Gessler, A., Hacke, U.
G., Hakamada, R., Hector, A., Jenkins, M. W., Kane, J. M., Kolb, T. E., Law,
D. J., Lewis, J. D., Limousin, J.-M., Love, D. M., Macalady, A. K.,
Martínez-Vilalta, J., Mencuccini, M., Mitchell, P. J., Muss, J. D.,
O'Brien, M. J., O'Grady, A. P., Pangle, R. E., Pinkard, E. A., Piper, F. I.,
Plaut, J. A., Pockman, W. T., Quirk, J., Reinhardt, K., Ripullone, F., Ryan,
M. G., Sala, A., Sevanto, S., Sperry, J. S., Vargas, R., Vennetier, M., Way,
D. A., Xu, C., Yepez, E. A., and McDowell, N. G.: A multi-species synthesis
of physiological mechanisms in drought-induced tree mortality, Nat. Ecol.
Evol., 1, 1285–1291, https://doi.org/10.1038/s41559-017-0248-x, 2017.
Allen, C. D., Breshears, D. D., and McDowell, N. G.: On underestimation of
global vulnerability to tree mortality and forest die-off from hotter
drought in the Anthropocene, Ecosphere, 6, art129,
https://doi.org/10.1890/ES15-00203.1, 2015.
Arend, M., Link, R. M., Zahnd, C., Hoch, G., Schuldt, B., and Kahmen, A.:
Lack of hydraulic recovery as a cause of post-drought foliage reduction and
canopy decline in European beech, New Phytol., 234, 1195–1205,
https://doi.org/10.1111/nph.18065, 2022.
Bartlett, M. K., Scoffoni, C., and Sack, L.: The determinants of leaf turgor
loss point and prediction of drought tolerance of species and biomes: A
global meta-analysis, Ecol. Lett., 15, 393–405,
https://doi.org/10.1111/j.1461-0248.2012.01751.x, 2012.
Bartlett, M. K., Klein, T., Jansen, S., Choat, B., and Sack, L.: The
correlations and sequence of plant stomatal, hydraulic, and wilting
responses to drought, P. Natl. Acad. Sci. USA, 113, 13098–13103,
https://doi.org/10.1073/pnas.1604088113, 2016.
Billon, L. M., Blackman, C. J., Cochard, H., Badel, E., Hitmi, A.,
Cartailler, J., Souchal, R., and Torres-Ruiz, J. M.: The DroughtBox: A new
tool for phenotyping residual branch conductance and its temperature
dependence during drought, Plant Cell Environ., 43, 1584–1594,
https://doi.org/10.1111/pce.13750, 2020.
Brodribb, T. J., Powers, J., Cochard, H., and Choat, B.: Hanging by a
thread? Forests and drought, Science, 368, 261–266, https://doi.org/10.1126/science.aat7631, 2020.
Cheaib, A., Badeau, V., Boe, J., Chuine, I., Delire, C., Dufrêne, E.,
François, C., Gritti, E. S., Legay, M., Pagé, C., Thuiller, W.,
Viovy, N., and Leadley, P.: Climate change impacts on tree ranges: Model
intercomparison facilitates understanding and quantification of uncertainty,
Ecol. Lett., 15, 533–544, https://doi.org/10.1111/j.1461-0248.2012.01764.x,
2012.
Choat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., Bucci, S. J., Feild, T.
S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L., Lens, F., Maherali, H., Martínez-Vilalta, J.,
Mayr, S., Mencuccini, M., Mitchell, P. J., Nardini, A., Pittermann, J., Pratt, R. B., Sperry, J. S.,
Westoby, M., Wright, I. J., and Zanne, A. E.: Global convergence in the vulnerability of forests
to drought, Nature, 491, 4–8, https://doi.org/10.1038/nature11688, 2012.
Choat, B., Brodribb, T. J., Brodersen, C. R., Duursma, R. A., López, R., and Medlyn, B. E.:
Triggers of tree mortality under drought, Nature, 558, 531–539,
https://doi.org/10.1038/s41586-018-0240-x, 2018.
Christoffersen, B. O., Gloor, M., Fauset, S., Fyllas, N. M., Galbraith, D. R., Baker, T. R., Kruijt, B., Rowland, L., Fisher, R. A., Binks, O. J., Sevanto, S., Xu, C., Jansen, S., Choat, B., Mencuccini, M., McDowell, N. G., and Meir, P.: Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro), Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, 2016.
Chuine, I. and Cour, P.: Climatic determinants of budburst seasonality in
four temperate-zone tree species, New Phytol., 143, 339–349, 1999.
Cochard, H.: A new mechanism for tree mortality due to drought and
heatwaves, Peer Community J., 1, e36, https://doi.org/10.24072/PCJOURNAL.45,
2021.
Cochard, H., Pimont, F., Ruffault, J., and Martin-StPaul, N.: SurEau: a
mechanistic model of plant water relations under extreme drought, Ann. For.
Sci., 78, 1–23, https://doi.org/10.1007/s13595-021-01067-y, 2021.
Couvreur, V., Ledder, G., Manzoni, S., Way, D. A., Muller, E. B., and Russo,
S. E.: Water transport through tall trees: A vertically explicit, analytical
model of xylem hydraulic conductance in stems, Plant Cell Environ., 41,
1821–1839, https://doi.org/10.1111/pce.13322, 2018.
Cruiziat, P., Cochard, H., and Améglio, T.: Hydraulic architecture of
trees: main concepts and results, Ann. For. Sci., 59, 723–752,
https://doi.org/10.1051/forest:2002060, 2002.
De Cáceres, M., Martínez-Vilalta, J., Coll, L., Llorens, P.,
Casals, P., Poyatos, R., Pausas, J. G., and Brotons, L.: Coupling a water
balance model with forest inventory data to predict drought stress: The role
of forest structural changes vs. climate changes, Agric. For. Meteorol.,
213, 77–90, https://doi.org/10.1016/j.agrformet.2015.06.012, 2015.
De Cáceres, M., Mencuccini, M., Martin-StPaul, N., Limousin, J. M.,
Coll, L., Poyatos, R., Cabon, A., Granda, V., Forner, A., Valladares, F.,
and Martínez-Vilalta, J.: Unravelling the effect of species mixing on
water use and drought stress in Mediterranean forests: A modelling approach,
Agric. For. Meteorol., 296, 108233, https://doi.org/10.1016/j.agrformet.2020.108233,
2021.
De Kauwe, M. G., Medlyn, B. E., Ukkola, A. M., Mu, M., Sabot, M. E. B.,
Pitman, A. J., Meir, P., Cernusak, L. A., Rifai, S. W., Choat, B., Tissue,
D. T., Blackman, C. J., Li, X., Roderick, M., and Briggs, P. R.: Identifying
areas at risk of drought-induced tree mortality across South-Eastern
Australia, Glob. Change Biol., 26, 5716–5733, https://doi.org/10.1111/gcb.15215,
2020.
Domec, J. C., Smith, D. D., and McCulloh, K. A.: A synthesis of the effects
of atmospheric carbon dioxide enrichment on plant hydraulics: implications
for whole-plant water use efficiency and resistance to drought, Plant Cell
Environ., 40, 921–937, https://doi.org/10.1111/pce.12843, 2017.
Dufour-Kowalski, S., Courbaud, B., Dreyfus, P., Meredieu, C., and De
Coligny, F.: Capsis: An open software framework and community for forest
growth modelling, Ann. Forest Sci., 221–233,
https://doi.org/10.1007/s13595-011-0140-9, 2012.
Dufrêne, E., Davi, H., François, C., Maire, G. le, Dantec, V. L.,
and Granier, A.: Modelling carbon and water cycles in a beech forest: Part
I: Model description and uncertainty analysis on modelled NEE, Ecol. Model.,
185, 407–436, https://doi.org/10.1016/j.ecolmodel.2005.01.004, 2005.
Dutykh, D.: How to overcome the Courant-Friedrichs-Lewy condition of
explicit discretizations?, Numer. Methods Diffus. Phenom. Build. Phys.,
103–120, https://doi.org/10.1007/978-3-030-31574-0_5, 2016.
Duursma, R. A., Blackman, C. J., Lopéz, R., Martin-StPaul, N. K.,
Cochard, H., and Medlyn, B. E.: On the minimum leaf conductance: its role in
models of plant water use, and ecological and environmental controls, New
Phytol., 221, 693–705, https://doi.org/10.1111/nph.15395, 2019.
Fargeon, H., Pimont, F., Martin-StPaul, N., De Caceres, M., Ruffault, J.,
Barbero, R., and Dupuy, J. L.: Projections of fire danger under climate
change over France: where do the greatest uncertainties lie?, Clim. Change,
160, 479–493, https://doi.org/10.1007/s10584-019-02629-w, 2020.
Fatichi, S., Pappas, C., and Ivanov, V. Y.: Modeling plant–water
interactions: an ecohydrological overview from the cell to the global scale,
Wiley Interdiscip. Rev. Water, 3, 327–368,
https://doi.org/10.1002/wat2.1125, 2016.
Feng, X., Ackerly, D. D., Dawson, T. E., Manzoni, S., Skelton, R. P., Vico,
G., and Thompson, S. E.: The ecohydrological context of drought and
classification of plant responses, Ecol. Lett., 21, 1723–1736,
https://doi.org/10.1111/ele.13139, 2018.
Fettig, C. J., Mortenson, L. A., Bulaon, B. M., and Foulk, P. B.: Tree
mortality following drought in the central and southern Sierra Nevada,
California, U.S., For. Ecol. Manag., 432, 164–178,
https://doi.org/10.1016/j.foreco.2018.09.006, 2019.
Granier, A., Bréda, N., Biron, P., and Villette, S.: A lumped water
balance model to evaluate duration and intensity of drought constraints in
forest stands, Ecol. Model., 116, 269–283,
https://doi.org/10.1016/S0304-3800(98)00205-1, 1999.
Guillemot, J., Martin-StPaul, N. K., Bulascoschi, L., Poorter, L., Morin,
X., Pinho, B. X., le Maire, G., R. L. Bittencourt, P., Oliveira, R. S.,
Bongers, F., Brouwer, R., Pereira, L., Gonzalez Melo, G. A., Boonman, C. C.
F., Brown, K. A., Cerabolini, B. E. L., Niinemets, Ü., Onoda, Y.,
Schneider, J. V., Sheremetiev, S., and Brancalion, P. H. S.: Small and slow
is safe: On the drought tolerance of tropical tree species, Glob. Change
Biol., 28, 2622–2638, https://doi.org/10.1111/gcb.16082, 2022.
Gupta, S., Hengl, T., Lehmann, P., Bonetti, S., and Or, D.: SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications, Earth Syst. Sci. Data, 13, 1593–1612, https://doi.org/10.5194/essd-13-1593-2021, 2021.
Hengl, T., Jesus, J. M. de, Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda,
M., Blagotiæ, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Hoff, C. and Rambal, S.: An examination of the interaction between climate,
soil and leaf area index in a Quercus ilex ecosystem, Ann. For. Sci., 60,
153–161, https://doi.org/10.1051/forest:2003008, 2003.
Hölttä, T., Cochard, H., Nikinmaa, E., and Mencuccini, M.:
Capacitive effect of cavitation in xylem conduits: results from a dynamic
model, Plant Cell Environ., 32, 10–21, 2009.
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E.,
and Schulze, E. D.: A global analysis of root distributions for terrestrial
biomes, Oecologia, 108, 389–411, https://doi.org/10.1007/BF00333714, 1996.
Jactel, H., Petit, J., Desprez-Loustau, M. L., Delzon, S., Piou, D.,
Battisti, A., and Koricheva, J.: Drought effects on damage by forest insects
and pathogens: A meta-analysis, Glob. Change Biol., 18, 267–276,
https://doi.org/10.1111/j.1365-2486.2011.02512.x, 2012.
Jarvis, P. G.: The interpretation of the variations in leaf water potential
and stomatal conductance found in canopies in the field, Philos. T. Roy.
Soc. Lond. B, 273, 593–610,
https://doi.org/10.1098/rstb.1976.0035, 1976.
Jones, H. G.: Plants and microclimate: A quantitative approach to
environmental plant physiology, Cambridge University Press,
https://doi.org/10.1017/CBO9780511845727, 2013.
Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., Lola
da Costa, A. C., and Gentine, P.: Implementing Plant Hydraulics in the
Community Land Model, Version 5, J. Adv. Model. Earth Syst., 11, 485–513,
https://doi.org/10.1029/2018MS001500, 2019.
Klein, T.: The variability of stomatal sensitivity to leaf water potential
across tree species indicates a continuum between isohydric and anisohydric
behaviours, Funct. Ecol., 28, 1313–1320,
https://doi.org/10.1111/1365-2435.12289, 2014.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
Lemaire, C., Blackman, C. J., Cochard, H., Menezes-Silva, P. E.,
Torres-Ruiz, J. M., and Herbette, S.: Acclimation of hydraulic and
morphological traits to water deficit delays hydraulic failure during
simulated drought in poplar, Tree Physiol., 41, 2008–2021, 2021.
Lens, F., Picon-Cochard, C., Delmas, C. E. L., Signarbieux, C., Buttler, A., Cochard, H., Jansen,
S., Chauvin, T., Doria, L. C., Del Arco, M., and Delzon, S.: Herbaceous angiosperms are not
more vulnerable to drought-induced embolism than angiosperm trees, Plant Physiol., 172,
661–667, https://doi.org/10.1104/PP.16.00829, 2016.
Li, L., Yang, Z., Matheny, A. M., Zheng, H., Swenson, S. C., Lawrence, D.
M., Barlage, M., Yan, B., McDowell, N. G., and Leung, L. R.: Representation
of Plant Hydraulics in the Noah-MP Land Surface Model: Model Development and
Multi-scale Evaluation, J. Adv. Model. Earth Syst., 13, e2020MS002214,
https://doi.org/10.1029/2020ms002214, 2021.
Lin, Y.-S., Medlyn, B. E., Duursma, R. A., Prentice, I. C., Wang, H., Baig,
S., Eamus, D., de Dios, V. R., Mitchell, P., Ellsworth, D. S., de Beeck, M.
O., Wallin, G., Uddling, J., Tarvainen, L., Linderson, M.-L., Cernusak, L.
A., Nippert, J. B., Ocheltree, T. W., Tissue, D. T., Martin-StPaul, N. K.,
Rogers, A., Warren, J. M., De Angelis, P., Hikosaka, K., Han, Q., Onoda, Y.,
Gimeno, T. E., Barton, C. V. M., Bennie, J., Bonal, D., Bosc, A., Löw,
M., Macinins-Ng, C., Rey, A., Rowland, L., Setterfield, S. A., Tausz-Posch,
S., Zaragoza-Castells, J., Broadmeadow, M. S. J., Drake, J. E., Freeman, M.,
Ghannoum, O., Hutley, L. B., Kelly, J. W., Kikuzawa, K., Kolari, P., Koyama,
K., Limousin, J.-M., Meir, P., Lola da Costa, A. C., Mikkelsen, T. N.,
Salinas, N., Sun, W., and Wingate, L.: Optimal stomatal behaviour around the
world, Nat. Clim. Change, 5, 459–464, https://doi.org/10.1038/nclimate2550,
2015.
López, R., Cano, F. J., Martin-StPaul, N. K., Cochard, H., and Choat,
B.: Coordination of stem and leaf traits define different strategies to
regulate water loss and tolerance ranges to aridity, New Phytol., 230,
497–509, 2021.
Mackay, D. S., Ahl, D. E., Ewers, B. E., Samanta, S., Gower, S. T., and
Burrows, S. N.: Physiological tradeoffs in the parameterization of a model
of canopy transpiration, Adv. Water Resour., 26, 179–194,
https://doi.org/10.1016/S0309-1708(02)00090-8, 2003.
Mantova, M., Menezes-Silva, P. E., Badel, E., Cochard, H., and Torres-Ruiz,
J. M.: The interplay of hydraulic failure and cell vitality explains tree
capacity to recover from drought, Physiol. Plant., 172, 13331,
https://doi.org/10.1111/ppl.13331, 2021.
Martinez-Vilalta, J., Anderegg, W. R. L., Sapes, G., and Sala, A.: Greater
focus on water pools may improve our ability to understand and anticipate
drought-induced mortality in plants, New Phytol., 223, 22–32,
https://doi.org/10.1111/nph.15644, 2019.
Martin-StPaul, N., Delzon, S., and Cochard, H.: Plant resistance to drought
depends on timely stomatal closure, Ecol. Lett., 20, 1437–1447,
https://doi.org/10.1111/ele.12851, 2017.
McDowell, N. G., Brodribb, T. J., and Nardini, A.: Hydraulics in the 21st
century, New Phytol., 224, 537–542, https://doi.org/10.1111/nph.16151,
2019.
McDowell, N. G., Sapes, G., Pivovaroff, A., Adams, H. D., Allen, C. D.,
Anderegg, W. R. L., Arend, M., Breshears, D. D., Brodribb, T., Choat, B.,
Cochard, H., De Cáceres, M., De Kauwe, M. G., Grossiord, C., Hammond, W.
M., Hartmann, H., Hoch, G., Kahmen, A., Klein, T., Mackay, D. S., Mantova,
M., Martínez-Vilalta, J., Medlyn, B. E., Mencuccini, M., Nardini, A.,
Oliveira, R. S., Sala, A., Tissue, D. T., Torres-Ruiz, J. M., Trowbridge, A.
M., Trugman, A. T., Wiley, E., and Xu, C.: Mechanisms of woody-plant
mortality under rising drought, CO2 and vapour pressure deficit, Nat. Rev.
Earth Environ., 3, 294–308, https://doi.org/10.1038/s43017-022-00272-1,
2022.
Meinzer, F. C., Woodruff, D. R., Domec, J.-C., Goldstein, G., Campanello, P.
I., Gatti, M. G., and Villalobos-Vega, R.: Coordination of leaf and stem
water transport properties in tropical forest trees, Oecologia, 156, 31–41,
https://doi.org/10.1007/s00442-008-0974-5, 2008.
Meir, P., Meir, P., Mencuccini, M., and Dewar, R. C.: Tansley insight
Drought-related tree mortality: addressing the gaps in understanding and
prediction, New Phytol., 207, 28–33, 2015.
Mencuccini, M., Manzoni, S., and Christoffersen, B.: Modelling water fluxes
in plants: from tissues to biosphere, New Phytol., 222, 1207–1222,
https://doi.org/10.1111/nph.15681, 2019.
Moreaux, V., Martel, S., Bosc, A., Picart, D., Achat, D., Moisy, C., Aussenac, R., Chipeaux, C., Bonnefond, J.-M., Figuères, S., Trichet, P., Vezy, R., Badeau, V., Longdoz, B., Granier, A., Roupsard, O., Nicolas, M., Pilegaard, K., Matteucci, G., Jolivet, C., Black, A. T., Picard, O., and Loustau, D.: Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0, Geosci. Model Dev., 13, 5973–6009, https://doi.org/10.5194/gmd-13-5973-2020, 2020.
Morin, X., Bugmann, H., de Coligny, F., Martin-StPaul, N., Cailleret, M.,
Limousin, J.-M., Ourcival, J.-M., Prevosto, B., Simioni, G., Toigo, M.,
Vennetier, M., Catteau, E., and Guillemot, J.: Beyond forest succession: A
gap model to study ecosystem functioning and tree community composition
under climate change, Funct. Ecol., 35, 955–975,
https://doi.org/10.1111/1365-2435.13760, 2021.
Mouillot, F., Rambal, S., and Lavorel, S.: A generic process-based SImulator
for meditERRanean landscApes (SIERRA): design and validation exercises, For.
Ecol. Manag., 147, 75–97, 2001.
Nolan, R. H., Blackman, C. J., de Dios, V. R., Choat, B., Medlyn, B. E., Li,
X., Bradstock, R. A., and Boer, M. M.: Linking Forest Flammability and Plant
Vulnerability to Drought, Forests, 11, 779,
https://doi.org/10.3390/f11070779, 2020.
Pammenter, N. W. and Vander Willigen, C.: A mathematical and statistical
analysis of the curves illustrating vulnerability of xylem to cavitation,
Tree Physiol., 18, 589–593, 1998.
Pimont, F., Ruffault, J., Martin-StPaul, N. K., and Dupuy, J.-L.: Why is the effect of live fuel
moisture content on fire rate of spread underestimated in field experiments in shrublands?,
Int. J. Wildland Fire, 28, 127–137, https://doi.org/10.1071/WF18091, 2019.
Rambal, S.: The differential role of mechanisms for drought resistance in a
Mediterranean evergreen shrub: a simulation approach, Plant Cell Environ.,
16, 35–44, https://doi.org/10.1111/j.1365-3040.1993.tb00842.x, 1993.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for
Statistical Computing, Vienna, Austria, 2020.
Riederer, M. and Schreiber, L.: Protecting against water loss: analysis of
the barrier properties of plant cuticles, J. Exp. Bot., 52, 2023–2032,
https://doi.org/10.1093/jexbot/52.363.2023, 2001.
Rowland, L., Martínez-Vilalta, J., and Mencuccini, M.: Hard times for
high expectations from hydraulics: predicting drought-induced forest
mortality at landscape scales remains a challenge, New Phytol., 230,
1685–1687, https://doi.org/10.1111/nph.17317, 2021.
Ruffault, J., Martin-StPaul, N. K. N., Rambal, S., and Mouillot, F.:
Differential regional responses in drought length, intensity and timing to
recent climate changes in a Mediterranean forested ecosystem, Clim. Change,
117, 103–117, https://doi.org/10.1007/s10584-012-0559-5, 2013.
Ruffault, J., Martin-StPaul, N. K., Duffet, C., Goge, F., and Mouillot, F.:
Projecting future drought in Mediterranean forests: Bias correction of
climate models matters!, Theor. Appl. Climatol., 117, 113–122,
https://doi.org/10.1007/s00704-013-0992-z, 2014.
Ruffault, J., Curt, T., Martin-StPaul, N. K., Moron, V., and Trigo, R. M.: Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean, Nat. Hazards Earth Syst. Sci., 18, 847–856, https://doi.org/10.5194/nhess-18-847-2018, 2018a.
Ruffault, J., Martin-StPaul, N., Pimont, F., and Dupuy, J.-L.: How well do
meteorological drought indices predict live fuel moisture content (LFMC)? An
assessment for wildfire research and operations in Mediterranean ecosystems,
Agric. For. Meteorol., 262, 391–401,
https://doi.org/10.1016/j.agrformet.2018.07.031, 2018b.
Ruffault, J., Curt, T., Moron, V., Trigo, R. M., Mouillot, F., Koutsias, N.,
Pimont, F., Martin-StPaul, N. K., Barbero, R., Dupuy, J. L., Russo, A., and
Belhadj-Kheder, C.: Increased likelihood of heat-induced large wildfires in
the Mediterranean Basin, Sci. Rep.-UK, 10, 13790,
https://doi.org/10.1101/2020.01.09.896878, 2020.
Ruffault, J., Martin-StPaul, N., and Pimont, F.: SurEau-Ecos v2.0.1 (v2.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.5878978, 2022.
Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A.,
Gharun, M., Grams, T. E. E., Hauck, M., Hajek, P., Hartmann, H.,
Hiltbrunner, E., Hoch, G., Holloway-Phillips, M., Körner, C., Larysch,
E., Lübbe, T., Nelson, D. B., Rammig, A., Rigling, A., Rose, L., Ruehr,
N. K., Schumann, K., Weiser, F., Werner, C., Wohlgemuth, T., Zang, C. S.,
and Kahmen, A.: A first assessment of the impact of the extreme 2018 summer
drought on Central European forests, Basic Appl. Ecol., 45, 86–103,
https://doi.org/10.1016/j.baae.2020.04.003, 2020.
Seidl, R., Schelhaas, M.-J., Rammer, W., and Verkerk, P. J.: Increasing
forest disturbances in Europe and their impact on carbon storage, Nat. Clim.
Change, 4, 806–810, 2014.
Sergent, A. S., Varela, S. A., Barigah, T. S., Badel, E., Cochard, H., Dalla-Salda, G., Delzon, S.,
Fernández, M. E., Guillemot, J., Gyenge, J., Lamarque, L. J., Martinez-Meier, A., Rozenberg,
P., Torres-Ruiz, J. M., and Martin-StPaul, N. K.: A comparison of five methods to assess
embolism resistance in trees, For. Ecol. Manag., 468, 118175,
https://doi.org/10.1016/j.foreco.2020.118175, 2020.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., and Sykes, M. T.: Evaluation of
ecosystem dynamics, plant geography and terrestrial carbon cycling in the
LPJ dynamic global vegetation model, Glob.
Change Biol., 9, 161–185, 2003.
Sobol, I. M.: Global sensitivity indices for nonlinear mathematical models
and their Monte Carlo estimates, Math. Comput. Simul., 55, 271–280, 2001.
Sperry, J. S., Adler, F. R., Campbell, G. S., and Comstock, J. P.:
Limitation of plant water use by rhizosphere and xylem conductance: results
from a model, Plant Cell Environ., 21, 347–359,
https://doi.org/10.1046/j.1365-3040.1998.00287.x, 1998.
Sperry, J. S., Venturas, M. D., Anderegg, W. R. L., Mencuccini, M., Mackay,
D. S., Wang, Y., and Love, D. M.: Predicting stomatal responses to the
environment from the optimization of photosynthetic gain and hydraulic cost,
Plant Cell Environ., 40, 816–830, 2017.
Sterck, F., Markesteijn, L., Schieving, F., and Poorter, L.: Functional
traits determine trade-offs and niches in a tropical forest community, P.
Natl. Acad. Sci. USA, 108, 20627–20632,
https://doi.org/10.1073/pnas.1106950108, 2011.
Tóth, B., Weynants, M., Pásztor, L., and Hengl, T.: 3D soil hydraulic database of Europe at
250 m resolution, Hydrol. Process., 31, 2662–2666, https://doi.org/10.1002/hyp.11203,
2017.
Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich,
J., Briffa, K. R., and Sheffield, J.: Global warming and changes in drought,
Nat. Clim. Change, 4, 17–22, 2014.
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J., and
Stephenson, N. L.: Why is Tree Drought Mortality so Hard to Predict?, Trends
Ecol. Evol., 36, 520–532, https://doi.org/10.1016/j.tree.2021.02.001, 2021.
Tuzet, A., Granier, A., Betsch, P., Peiffer, M., and Perrier, A.: Modelling
hydraulic functioning of an adult beech stand under non-limiting soil water
and severe drought condition, Ecol. Model., 348, 56–77,
https://doi.org/10.1016/j.ecolmodel.2017.01.007, 2017.
Tyree, M. T. and Ewers, F. W.: The hydraulic architecture of trees and other
woody plants, New Phytol., 119, 345–360,
https://doi.org/10.1111/j.1469-8137.1991.tb00035.x, 1991.
Tyree, M. T. and Hammel, H. T.: The Measurement of the turgor pressure and
the water relations of plants by the pressure-bomb technique, J. Exp. Bot.,
23, 267–282, https://doi.org/10.1093/jxb/23.1.267, 1972.
Tyree, M. T. and Yang, S.: Water-storage capacity ofThuja, Tsuga andAcer
stems measured by dehydration isotherms, Planta, 182, 420–426, 1990.
van Genuchten, M. T.: A closed-form equation for predicting the hydraulic
conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892,
https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Venturas, M. D., Todd, H. N., Trugman, A. T., and Anderegg, W. R. L.:
Understanding and predicting forest mortality in the western United States
using long-term forest inventory data and modeled hydraulic damage, New
Phytol., 230, 1896–1910, https://doi.org/10.1111/nph.17043, 2020.
Vidal, J. P., Martin, E., Franchistéguy, L., Baillon, M., and
Soubeyroux, J. M.: A 50 year high resolution atmospheric reanalysis over
France with the Safran system, Int. J. Climatol., 30, 1627–1644, 2010.
Wang, Y., Sperry, J. S., Anderegg, W. R. L. L., Venturas, M. D., and
Trugman, A. T.: A theoretical and empirical assessment of stomatal
optimization modeling, New Phytol., 227, 311–325,
https://doi.org/10.1111/nph.16572, 2020.
Williams, M., Rastetter, E. B., Fernandes, D. N., Goulden, M. L., Wofsy, S.
C., Shaver, G. R., Melillo, J. M., Munger, J. W., Fan, S. M., and
Nadelhoffer, K. J.: Modelling the soil-plant-atmosphere continuum in a
Quercus-acer stand at Harvard forest: The regulation of stomatal conductance
by light, nitrogen and soil/plant hydraulic properties, Plant Cell Environ.,
19, 911–927, https://doi.org/10.1111/j.1365-3040.1996.tb00456.x, 1996.
Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M., and Guan, K.: Diversity
in plant hydraulic traits explains seasonal and inter-annual variations of
vegetation dynamics in seasonally dry tropical forests, New Phytol., 212,
80–95, https://doi.org/10.1111/nph.14009, 2016.
Short summary
A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
A widespread increase in tree mortality has been observed around the globe, and this trend is...
